inmoose.edgepy.mglmLevenberg

inmoose.edgepy.mglmLevenberg(y, design, dispersion=0, offset=0, weights=None, coef_start=None, start_method='null', maxit=200, tol=1e-06)

Fit genewise negative binomial GLMs with log-link using Levenberg damping to ensure convergence.

This is a low-level work-horse used by higher-level functions, especially glmFit(). Most users will not need to call this function directly.

This function fits a negative binomial GLM to each row of y. The row-wise GLMs all have the same design matrix but possibly different dispersions, offsets and weights. It is low-level in that it operates on atomic objects (matrices and vectors).

This function fits an arbitrary log-linear model to each response vector. It implements a Levenberg-Marquardt modification of the GLM scoring algorithm to prevent divergence. It treats the dispersion parameter of the negative binomial distribution as a known input.

Parameters:

  • y (array_like) – matrix of negative binomial counts. Rows for genes and columns for libraries.

  • design (array_like) – design matrix of the GLM. Assumed to be full column rank

  • dispersion (float or array_like) – scalar or vector giving the dispersion parameter for each GLM. Can be a scalar giving one value for all genes, or a vector of length equal to the number of genes giving genewise dispersions.

  • offset (array_like) – vector or matrix giving the offset that is to be included in the log linear model predictor. Can be a scalar, a vector of length equal to the number of libraries, or a matrix of the same shape as y.

  • weights (matrix, optional) – vector or matrix of non-negative quantitative weights. Can be a vector of length equal to the number of libraries, or a matrix of the same shape as y.

  • coef_start (array_like, optional) – matrix of starting values for the linear model coefficient. Number of rows should agree with y and number of columns should agree with design. This argument does not usually need to be set as the automatic starting values perform well.

  • start_method (str) – method used to generate starting values when coef_start = None. Possible values are “null” to start from the null model of equal expression levels or y to use the data as starting value for the mean.

  • maxit (int) – the maximum number of iterations for the Fisher scoring algorithm. The iteration will be stopped when this limit is reached even if the convergence criterion has not been satisfied.

  • tol (float) – the convergence tolerance.

Returns:

tuple with the following components:

  • matrix of estimated coefficients for the linear models

  • matrix of fitted values

  • vector of residual deviances

  • number of iterations used

  • Boolean vector indicating genes for which the maximum damping was exceeded before convergence was achieved

Return type:

tuple